# skfolio.descriptor.ReturnOnAssets ### *class* skfolio.descriptor.ReturnOnAssets Return on assets (ROA) descriptor. Computes the ratio of trailing twelve-month net income to total assets: $$ \text{ROA}(t) = \frac{\text{net\_income\_ttm}(t)}{\text{total\_assets}(t)} $$ Return on assets measures how efficiently a firm converts its asset base into earnings. Higher values indicate greater profitability per unit of capital deployed [[1]](#rb522965354ed-1). * **Parameters:** **None** * **Attributes:** **n_assets_** *int* : Number of assets seen during fitting. **asset_names_** *ndarray of shape (n_assets,)* : Asset names seen during fitting. ### Methods | [`fit_transform`](#skfolio.descriptor.ReturnOnAssets.fit_transform)(X[, y]) | Compute return on assets. | |--------------------------------------------------------------------------------|------------------------------------------------| | [`get_metadata_routing`](#skfolio.descriptor.ReturnOnAssets.get_metadata_routing)() | Get metadata routing of this object. | | [`get_params`](#skfolio.descriptor.ReturnOnAssets.get_params)([deep]) | Get parameters for this estimator. | | [`partial_fit_transform`](#skfolio.descriptor.ReturnOnAssets.partial_fit_transform)(X[, y]) | Stateless class delegation to `fit_transform`. | | [`set_params`](#skfolio.descriptor.ReturnOnAssets.set_params)(\*\*params) | Set the parameters of this estimator. | #### SEE ALSO [`ReturnOnEquity`](https://skfolio.org/generated/skfolio.descriptor.ReturnOnEquity.html.md#skfolio.descriptor.ReturnOnEquity) : Profitability per unit of equity. [`AssetTurnover`](https://skfolio.org/generated/skfolio.descriptor.AssetTurnover.html.md#skfolio.descriptor.AssetTurnover) : Sales generated per unit of total assets. ### Notes Net income can be negative, so $ROA$ can be negative. Negative values distinguish profitable from unprofitable firms. ### References * **[1]** “A five-factor asset pricing model” Journal of Financial Economics. Fama, E. F., & French, K. R. (2015). ### Examples ```pycon >>> from skfolio.datasets import make_synthetic_characteristics >>> from skfolio.descriptor import ReturnOnAssets >>> >>> X = make_synthetic_characteristics() >>> >>> descriptor = ReturnOnAssets() >>> return_on_assets = descriptor.fit_transform(X) ``` #### fit_transform(X, y=None, \*\*fit_params) Compute return on assets. * **Parameters:** **X** *AssetPanel* : Input panel containing `net_income_ttm` and `total_assets`. **y** *None* : Ignored. Present for compatibility with scikit-learn’s API. **\*\*fit_params** *dict* : Additional fit parameters. Ignored. * **Returns:** **return_on_assets** *ndarray of shape (n_observations, n_assets)* : Net income divided by total assets for each observation and asset. #### get_metadata_routing() Get metadata routing of this object. Please check [User Guide](https://skfolio.org/user_guide/metadata_routing.html.md#metadata-routing) on how the routing mechanism works. * **Returns:** **routing** *MetadataRequest* : A `MetadataRequest` encapsulating routing information. #### get_params(deep=True) Get parameters for this estimator. * **Parameters:** **deep** *bool, default=True* : If True, will return the parameters for this estimator and contained subobjects that are estimators. * **Returns:** **params** *dict* : Parameter names mapped to their values. #### partial_fit_transform(X, y=None, \*\*fit_params) Stateless class delegation to `fit_transform`. #### set_params(\*\*params) Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as `Pipeline`). The latter have parameters of the form `__` so that it’s possible to update each component of a nested object. * **Parameters:** **\*\*params** *dict* : Estimator parameters. * **Returns:** **self** *estimator instance* : Estimator instance.